Overview

Dataset statistics

Number of variables15
Number of observations4357
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory544.6 KiB
Average record size in memory128.0 B

Variable types

Numeric15

Alerts

revenue_purc is highly overall correlated with volume_products_purc and 6 other fieldsHigh correlation
recency_days is highly overall correlated with purchases and 1 other fieldsHigh correlation
volume_products_purc is highly overall correlated with revenue_purc and 6 other fieldsHigh correlation
assort_products_purc is highly overall correlated with revenue_purc and 5 other fieldsHigh correlation
purchases is highly overall correlated with revenue_purc and 6 other fieldsHigh correlation
avg_period_purc is highly overall correlated with revenue_purc and 5 other fieldsHigh correlation
frequency_purc is highly overall correlated with recency_days and 2 other fieldsHigh correlation
volume_basket_size_purc is highly overall correlated with revenue_purc and 3 other fieldsHigh correlation
assort_basket_size_purc is highly overall correlated with assort_products_purcHigh correlation
avg_ticket_purc is highly overall correlated with revenue_purc and 3 other fieldsHigh correlation
returns is highly overall correlated with volume_products_ret and 1 other fieldsHigh correlation
volume_products_ret is highly overall correlated with returns and 1 other fieldsHigh correlation
revenue_ret is highly overall correlated with returns and 1 other fieldsHigh correlation
revenue_real is highly overall correlated with revenue_purc and 6 other fieldsHigh correlation
volume_products_purc is highly skewed (γ1 = 20.43890078)Skewed
volume_basket_size_purc is highly skewed (γ1 = 47.76152942)Skewed
avg_ticket_purc is highly skewed (γ1 = 41.54391694)Skewed
volume_products_ret is highly skewed (γ1 = 45.06522235)Skewed
revenue_ret is highly skewed (γ1 = 52.03122381)Skewed
revenue_real is highly skewed (γ1 = 21.62701738)Skewed
customer_id has unique valuesUnique
returns has 2824 (64.8%) zerosZeros
volume_products_ret has 2824 (64.8%) zerosZeros
revenue_ret has 2824 (64.8%) zerosZeros

Reproduction

Analysis started2023-05-18 21:49:01.014689
Analysis finished2023-05-18 21:50:06.419263
Duration1 minute and 5.4 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct4357
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15301.55
Minimum12346
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.1 KiB
2023-05-18T18:50:07.053006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum12346
5-th percentile12615.8
Q113815
median15301
Q316779
95-th percentile17985.2
Maximum18287
Range5941
Interquartile range (IQR)2964

Descriptive statistics

Standard deviation1721.6046
Coefficient of variation (CV)0.11251177
Kurtosis-1.1955146
Mean15301.55
Median Absolute Deviation (MAD)1482
Skewness0.00090465831
Sum66668854
Variance2963922.2
MonotonicityNot monotonic
2023-05-18T18:50:07.304925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
13618 1
 
< 0.1%
13568 1
 
< 0.1%
12450 1
 
< 0.1%
15334 1
 
< 0.1%
17562 1
 
< 0.1%
17879 1
 
< 0.1%
16050 1
 
< 0.1%
15909 1
 
< 0.1%
16448 1
 
< 0.1%
Other values (4347) 4347
99.8%
ValueCountFrequency (%)
12346 1
< 0.1%
12347 1
< 0.1%
12348 1
< 0.1%
12349 1
< 0.1%
12350 1
< 0.1%
12352 1
< 0.1%
12353 1
< 0.1%
12354 1
< 0.1%
12355 1
< 0.1%
12356 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18281 1
< 0.1%
18280 1
< 0.1%
18278 1
< 0.1%
18277 1
< 0.1%
18276 1
< 0.1%
18274 1
< 0.1%
18273 1
< 0.1%

revenue_purc
Real number (ℝ)

Distinct4247
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009.9243
Minimum0
Maximum279138.02
Zeros28
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size68.1 KiB
2023-05-18T18:50:07.570553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile108.022
Q1301.84
median662.08
Q31625.05
95-th percentile5728.268
Maximum279138.02
Range279138.02
Interquartile range (IQR)1323.21

Descriptive statistics

Standard deviation8885.318
Coefficient of variation (CV)4.4207226
Kurtosis491.72801
Mean2009.9243
Median Absolute Deviation (MAD)461.98
Skewness19.610576
Sum8757240.3
Variance78948876
MonotonicityNot monotonic
2023-05-18T18:50:07.790409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28
 
0.6%
76.32 4
 
0.1%
79.2 3
 
0.1%
440 3
 
0.1%
35.4 3
 
0.1%
113.5 3
 
0.1%
363.65 3
 
0.1%
324.24 2
 
< 0.1%
207.5 2
 
< 0.1%
110.38 2
 
< 0.1%
Other values (4237) 4304
98.8%
ValueCountFrequency (%)
0 28
0.6%
3.75 1
 
< 0.1%
5.9 1
 
< 0.1%
6.2 1
 
< 0.1%
12.75 1
 
< 0.1%
13.3 1
 
< 0.1%
15 2
 
< 0.1%
17 1
 
< 0.1%
20.8 2
 
< 0.1%
21.95 1
 
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
168472.5 1
< 0.1%
136275.72 1
< 0.1%
124564.53 1
< 0.1%
116729.63 1
< 0.1%
91062.38 1
< 0.1%
77183.6 1
< 0.1%
72882.09 1
< 0.1%

recency_days
Real number (ℝ)

Distinct305
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.157677
Minimum0
Maximum400
Zeros35
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size68.1 KiB
2023-05-18T18:50:08.071623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q117
median51
Q3148
95-th percentile318
Maximum400
Range400
Interquartile range (IQR)131

Descriptive statistics

Standard deviation102.88209
Coefficient of variation (CV)1.0926575
Kurtosis0.47470125
Mean94.157677
Median Absolute Deviation (MAD)41
Skewness1.2567747
Sum410245
Variance10584.724
MonotonicityNot monotonic
2023-05-18T18:50:08.324723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 103
 
2.4%
3 94
 
2.2%
4 94
 
2.2%
2 89
 
2.0%
8 79
 
1.8%
10 77
 
1.8%
17 74
 
1.7%
7 72
 
1.7%
9 70
 
1.6%
22 64
 
1.5%
Other values (295) 3541
81.3%
ValueCountFrequency (%)
0 35
 
0.8%
1 103
2.4%
2 89
2.0%
3 94
2.2%
4 94
2.2%
5 48
1.1%
7 72
1.7%
8 79
1.8%
9 70
1.6%
10 77
1.8%
ValueCountFrequency (%)
400 28
0.6%
373 17
0.4%
372 17
0.4%
371 6
 
0.1%
369 3
 
0.1%
368 5
 
0.1%
367 5
 
0.1%
366 10
 
0.2%
365 10
 
0.2%
364 6
 
0.1%

volume_products_purc
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1771
Distinct (%)40.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1183.1708
Minimum0
Maximum196844
Zeros28
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size68.1 KiB
2023-05-18T18:50:08.637181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile43
Q1157
median375
Q3985
95-th percentile3545.2
Maximum196844
Range196844
Interquartile range (IQR)828

Descriptive statistics

Standard deviation5030.4692
Coefficient of variation (CV)4.2516848
Kurtosis613.01191
Mean1183.1708
Median Absolute Deviation (MAD)275
Skewness20.438901
Sum5155075
Variance25305620
MonotonicityNot monotonic
2023-05-18T18:50:08.889312image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28
 
0.6%
88 19
 
0.4%
120 17
 
0.4%
84 16
 
0.4%
128 15
 
0.3%
144 15
 
0.3%
72 15
 
0.3%
146 15
 
0.3%
106 14
 
0.3%
150 14
 
0.3%
Other values (1761) 4189
96.1%
ValueCountFrequency (%)
0 28
0.6%
1 2
 
< 0.1%
2 6
 
0.1%
3 3
 
0.1%
4 7
 
0.2%
5 3
 
0.1%
6 3
 
0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
196844 1
< 0.1%
80997 1
< 0.1%
80179 1
< 0.1%
77373 1
< 0.1%
74215 1
< 0.1%
69993 1
< 0.1%
64549 1
< 0.1%
64124 1
< 0.1%
63312 1
< 0.1%
58343 1
< 0.1%

assort_products_purc
Real number (ℝ)

Distinct340
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.039247
Minimum0
Maximum1785
Zeros28
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size68.1 KiB
2023-05-18T18:50:09.188563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q115
median35
Q377
95-th percentile204
Maximum1785
Range1785
Interquartile range (IQR)62

Descriptive statistics

Standard deviation85.216252
Coefficient of variation (CV)1.3960895
Kurtosis99.712864
Mean61.039247
Median Absolute Deviation (MAD)24
Skewness6.9143595
Sum265948
Variance7261.8095
MonotonicityNot monotonic
2023-05-18T18:50:09.437043image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 94
 
2.2%
10 86
 
2.0%
8 79
 
1.8%
9 79
 
1.8%
11 78
 
1.8%
13 77
 
1.8%
15 75
 
1.7%
5 74
 
1.7%
7 73
 
1.7%
6 72
 
1.7%
Other values (330) 3570
81.9%
ValueCountFrequency (%)
0 28
 
0.6%
1 94
2.2%
2 51
1.2%
3 61
1.4%
4 52
1.2%
5 74
1.7%
6 72
1.7%
7 73
1.7%
8 79
1.8%
9 79
1.8%
ValueCountFrequency (%)
1785 1
< 0.1%
1766 1
< 0.1%
1322 1
< 0.1%
1118 1
< 0.1%
884 1
< 0.1%
816 1
< 0.1%
717 1
< 0.1%
713 1
< 0.1%
699 1
< 0.1%
636 1
< 0.1%

purchases
Real number (ℝ)

Distinct58
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2210236
Minimum0
Maximum206
Zeros28
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size68.1 KiB
2023-05-18T18:50:09.718275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q35
95-th percentile13
Maximum206
Range206
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.6220428
Coefficient of variation (CV)1.8057333
Kurtosis244.6913
Mean4.2210236
Median Absolute Deviation (MAD)1
Skewness11.955694
Sum18391
Variance58.095536
MonotonicityNot monotonic
2023-05-18T18:50:09.970687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1505
34.5%
2 827
19.0%
3 501
 
11.5%
4 395
 
9.1%
5 236
 
5.4%
6 173
 
4.0%
7 139
 
3.2%
8 98
 
2.2%
9 69
 
1.6%
10 54
 
1.2%
Other values (48) 360
 
8.3%
ValueCountFrequency (%)
0 28
 
0.6%
1 1505
34.5%
2 827
19.0%
3 501
 
11.5%
4 395
 
9.1%
5 236
 
5.4%
6 173
 
4.0%
7 139
 
3.2%
8 98
 
2.2%
9 69
 
1.6%
ValueCountFrequency (%)
206 1
< 0.1%
198 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 1
< 0.1%
90 1
< 0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
< 0.1%
60 1
< 0.1%

avg_period_purc
Real number (ℝ)

Distinct1155
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195.66201
Minimum1
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size68.1 KiB
2023-05-18T18:50:10.379518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17
Q148
median109.66667
Q3400
95-th percentile400
Maximum400
Range399
Interquartile range (IQR)352

Descriptive statistics

Standard deviation163.38709
Coefficient of variation (CV)0.83504762
Kurtosis-1.7281078
Mean195.66201
Median Absolute Deviation (MAD)85.333333
Skewness0.33140981
Sum852499.38
Variance26695.343
MonotonicityNot monotonic
2023-05-18T18:50:10.815426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400 1585
36.4%
70 21
 
0.5%
46 18
 
0.4%
55 17
 
0.4%
49 16
 
0.4%
31 16
 
0.4%
91 16
 
0.4%
21 15
 
0.3%
35 15
 
0.3%
42 15
 
0.3%
Other values (1145) 2623
60.2%
ValueCountFrequency (%)
1 9
0.2%
2 4
0.1%
2.88372093 1
 
< 0.1%
3 6
0.1%
3.330357143 1
 
< 0.1%
3.351351351 1
 
< 0.1%
4 4
0.1%
4.191011236 1
 
< 0.1%
4.275862069 1
 
< 0.1%
4.5 1
 
< 0.1%
ValueCountFrequency (%)
400 1585
36.4%
366 1
 
< 0.1%
365 1
 
< 0.1%
364 1
 
< 0.1%
363 1
 
< 0.1%
357 2
 
< 0.1%
356 1
 
< 0.1%
355 2
 
< 0.1%
352 1
 
< 0.1%
351 2
 
< 0.1%

frequency_purc
Real number (ℝ)

Distinct1222
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.024340246
Minimum0
Maximum1
Zeros28
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size68.1 KiB
2023-05-18T18:50:11.256028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0032051282
Q10.0076045627
median0.014705882
Q30.026865672
95-th percentile0.069663795
Maximum1
Range1
Interquartile range (IQR)0.019261109

Descriptive statistics

Standard deviation0.041207855
Coefficient of variation (CV)1.6929926
Kurtosis179.9869
Mean0.024340246
Median Absolute Deviation (MAD)0.0084165742
Skewness10.308712
Sum106.05045
Variance0.0016980873
MonotonicityNot monotonic
2023-05-18T18:50:11.695615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01851851852 33
 
0.8%
0 28
 
0.6%
0.05263157895 25
 
0.6%
0.01612903226 24
 
0.6%
0.01538461538 24
 
0.6%
0.03846153846 22
 
0.5%
0.01639344262 22
 
0.5%
0.015625 22
 
0.5%
0.04545454545 21
 
0.5%
0.025 20
 
0.5%
Other values (1212) 4116
94.5%
ValueCountFrequency (%)
0 28
0.6%
0.002673796791 16
0.4%
0.002680965147 16
0.4%
0.002688172043 6
 
0.1%
0.002702702703 2
 
< 0.1%
0.0027100271 5
 
0.1%
0.002717391304 5
 
0.1%
0.00272479564 9
 
0.2%
0.002732240437 10
 
0.2%
0.002739726027 6
 
0.1%
ValueCountFrequency (%)
1 2
< 0.1%
0.550802139 1
 
< 0.1%
0.5294117647 1
 
< 0.1%
0.5 4
0.1%
0.4 1
 
< 0.1%
0.3333333333 4
0.1%
0.3315508021 1
 
< 0.1%
0.3157894737 1
 
< 0.1%
0.2727272727 2
< 0.1%
0.2621621622 1
 
< 0.1%

volume_basket_size_purc
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2136
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean254.70335
Minimum0
Maximum74215
Zeros28
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size68.1 KiB
2023-05-18T18:50:12.056096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32
Q192
median161
Q3272
95-th percentile600
Maximum74215
Range74215
Interquartile range (IQR)180

Descriptive statistics

Standard deviation1313.1896
Coefficient of variation (CV)5.1557611
Kurtosis2513.3345
Mean254.70335
Median Absolute Deviation (MAD)82
Skewness47.761529
Sum1109742.5
Variance1724467
MonotonicityNot monotonic
2023-05-18T18:50:12.352948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28
 
0.6%
100 19
 
0.4%
88 18
 
0.4%
120 18
 
0.4%
73 17
 
0.4%
82 17
 
0.4%
136 17
 
0.4%
114 16
 
0.4%
60 16
 
0.4%
72 16
 
0.4%
Other values (2126) 4175
95.8%
ValueCountFrequency (%)
0 28
0.6%
1 3
 
0.1%
2 5
 
0.1%
3 3
 
0.1%
3.333333333 1
 
< 0.1%
4 7
 
0.2%
5 3
 
0.1%
5.333333333 1
 
< 0.1%
5.666666667 1
 
< 0.1%
6 3
 
0.1%
ValueCountFrequency (%)
74215 1
< 0.1%
40498.5 1
< 0.1%
7824 1
< 0.1%
6009.333333 1
< 0.1%
4300 1
< 0.1%
4282 1
< 0.1%
4280 1
< 0.1%
3906 1
< 0.1%
3868.65 1
< 0.1%
3028 1
< 0.1%

assort_basket_size_purc
Real number (ℝ)

Distinct941
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.683717
Minimum0
Maximum259
Zeros28
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size68.1 KiB
2023-05-18T18:50:12.637281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.852381
Q17.6666667
median14
Q324
95-th percentile51.1
Maximum259
Range259
Interquartile range (IQR)16.333333

Descriptive statistics

Standard deviation17.599326
Coefficient of variation (CV)0.94196064
Kurtosis21.356905
Mean18.683717
Median Absolute Deviation (MAD)7.5
Skewness3.2028354
Sum81404.956
Variance309.73628
MonotonicityNot monotonic
2023-05-18T18:50:12.902888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 93
 
2.1%
10 90
 
2.1%
8 89
 
2.0%
9 89
 
2.0%
7 85
 
2.0%
13 85
 
2.0%
6 81
 
1.9%
11 78
 
1.8%
5 77
 
1.8%
14 75
 
1.7%
Other values (931) 3515
80.7%
ValueCountFrequency (%)
0 28
0.6%
0.2 1
 
< 0.1%
0.25 3
 
0.1%
0.3333333333 7
 
0.2%
0.4 1
 
< 0.1%
0.4090909091 1
 
< 0.1%
0.5 12
0.3%
0.5454545455 1
 
< 0.1%
0.5555555556 1
 
< 0.1%
0.5714285714 1
 
< 0.1%
ValueCountFrequency (%)
259 1
< 0.1%
219 1
< 0.1%
177 1
< 0.1%
171 1
< 0.1%
155 1
< 0.1%
153 1
< 0.1%
148 2
< 0.1%
141 1
< 0.1%
131 1
< 0.1%
128 1
< 0.1%

avg_ticket_purc
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4254
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean414.41346
Minimum0
Maximum84236.25
Zeros28
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size68.1 KiB
2023-05-18T18:50:13.168613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile84.760667
Q1176.68
median289.24
Q3424.254
95-th percentile916.94
Maximum84236.25
Range84236.25
Interquartile range (IQR)247.574

Descriptive statistics

Standard deviation1796.467
Coefficient of variation (CV)4.3349629
Kurtosis1855.0399
Mean414.41346
Median Absolute Deviation (MAD)120.84
Skewness41.543917
Sum1805599.4
Variance3227293.6
MonotonicityNot monotonic
2023-05-18T18:50:13.402975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28
 
0.6%
76.32 4
 
0.1%
120 3
 
0.1%
79.2 3
 
0.1%
440 3
 
0.1%
35.4 3
 
0.1%
113.5 3
 
0.1%
114.56 2
 
< 0.1%
179 2
 
< 0.1%
594 2
 
< 0.1%
Other values (4244) 4304
98.8%
ValueCountFrequency (%)
0 28
0.6%
3.75 1
 
< 0.1%
5.9 1
 
< 0.1%
6.2 1
 
< 0.1%
9.14 1
 
< 0.1%
11.67 1
 
< 0.1%
12.75 1
 
< 0.1%
13.3 1
 
< 0.1%
15 2
 
< 0.1%
17 1
 
< 0.1%
ValueCountFrequency (%)
84236.25 1
< 0.1%
77183.6 1
< 0.1%
14844.76667 1
< 0.1%
13305.5 1
< 0.1%
9341.26 1
< 0.1%
6228.2265 1
< 0.1%
6207.67 1
< 0.1%
4873.81 1
< 0.1%
4366.78 1
< 0.1%
4327.621667 1
< 0.1%

returns
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.77622217
Minimum0
Maximum45
Zeros2824
Zeros (%)64.8%
Negative0
Negative (%)0.0%
Memory size68.1 KiB
2023-05-18T18:50:13.623522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum45
Range45
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.965778
Coefficient of variation (CV)2.532494
Kurtosis152.4758
Mean0.77622217
Median Absolute Deviation (MAD)0
Skewness9.1990888
Sum3382
Variance3.864283
MonotonicityNot monotonic
2023-05-18T18:50:13.826613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 2824
64.8%
1 875
 
20.1%
2 292
 
6.7%
3 140
 
3.2%
4 92
 
2.1%
5 37
 
0.8%
6 32
 
0.7%
7 21
 
0.5%
9 8
 
0.2%
11 5
 
0.1%
Other values (13) 31
 
0.7%
ValueCountFrequency (%)
0 2824
64.8%
1 875
 
20.1%
2 292
 
6.7%
3 140
 
3.2%
4 92
 
2.1%
5 37
 
0.8%
6 32
 
0.7%
7 21
 
0.5%
8 5
 
0.1%
9 8
 
0.2%
ValueCountFrequency (%)
45 1
 
< 0.1%
44 1
 
< 0.1%
35 1
 
< 0.1%
27 1
 
< 0.1%
21 1
 
< 0.1%
18 2
 
< 0.1%
17 1
 
< 0.1%
15 2
 
< 0.1%
14 1
 
< 0.1%
13 5
0.1%

volume_products_ret
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct218
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.850356
Minimum0
Maximum80995
Zeros2824
Zeros (%)64.8%
Negative0
Negative (%)0.0%
Memory size68.1 KiB
2023-05-18T18:50:14.092758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile60
Maximum80995
Range80995
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1685.8517
Coefficient of variation (CV)27.256944
Kurtosis2079.0948
Mean61.850356
Median Absolute Deviation (MAD)0
Skewness45.065222
Sum269482
Variance2842095.9
MonotonicityNot monotonic
2023-05-18T18:50:14.345713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2824
64.8%
1 175
 
4.0%
2 152
 
3.5%
3 105
 
2.4%
4 91
 
2.1%
6 79
 
1.8%
5 64
 
1.5%
12 55
 
1.3%
7 44
 
1.0%
8 43
 
1.0%
Other values (208) 725
 
16.6%
ValueCountFrequency (%)
0 2824
64.8%
1 175
 
4.0%
2 152
 
3.5%
3 105
 
2.4%
4 91
 
2.1%
5 64
 
1.5%
6 79
 
1.8%
7 44
 
1.0%
8 43
 
1.0%
9 42
 
1.0%
ValueCountFrequency (%)
80995 1
< 0.1%
74215 1
< 0.1%
9360 1
< 0.1%
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3331 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%

revenue_ret
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1102
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.88107
Minimum0
Maximum168469.6
Zeros2824
Zeros (%)64.8%
Negative0
Negative (%)0.0%
Memory size68.1 KiB
2023-05-18T18:50:14.611320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q315
95-th percentile150.4
Maximum168469.6
Range168469.6
Interquartile range (IQR)15

Descriptive statistics

Standard deviation2850.4684
Coefficient of variation (CV)26.17965
Kurtosis2918.7792
Mean108.88107
Median Absolute Deviation (MAD)0
Skewness52.031224
Sum474394.83
Variance8125170.1
MonotonicityNot monotonic
2023-05-18T18:50:14.882705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2824
64.8%
12.75 20
 
0.5%
4.95 19
 
0.4%
9.95 17
 
0.4%
15 17
 
0.4%
5.9 12
 
0.3%
25.5 11
 
0.3%
3.75 10
 
0.2%
4.25 10
 
0.2%
7.5 9
 
0.2%
Other values (1092) 1408
32.3%
ValueCountFrequency (%)
0 2824
64.8%
0.42 2
 
< 0.1%
0.65 1
 
< 0.1%
0.95 1
 
< 0.1%
1.25 5
 
0.1%
1.45 4
 
0.1%
1.64 1
 
< 0.1%
1.65 5
 
0.1%
1.7 2
 
< 0.1%
1.79 1
 
< 0.1%
ValueCountFrequency (%)
168469.6 1
< 0.1%
77183.6 1
< 0.1%
22998.4 1
< 0.1%
14688.24 1
< 0.1%
8511.15 1
< 0.1%
7393.59 1
< 0.1%
5228.4 1
< 0.1%
4815.26 1
< 0.1%
4814.74 1
< 0.1%
4486.24 1
< 0.1%

revenue_real
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4273
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1901.0433
Minimum-1192.2
Maximum278778.02
Zeros10
Zeros (%)0.2%
Negative30
Negative (%)0.7%
Memory size68.1 KiB
2023-05-18T18:50:15.163518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1192.2
5-th percentile101.94
Q1294.12
median645.72
Q31598.24
95-th percentile5630.694
Maximum278778.02
Range279970.22
Interquartile range (IQR)1304.12

Descriptive statistics

Standard deviation8270.8593
Coefficient of variation (CV)4.3506949
Kurtosis603.42176
Mean1901.0433
Median Absolute Deviation (MAD)451.35
Skewness21.627017
Sum8282845.5
Variance68407113
MonotonicityNot monotonic
2023-05-18T18:50:15.379092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10
 
0.2%
76.32 4
 
0.1%
15 3
 
0.1%
79.2 3
 
0.1%
113.5 3
 
0.1%
363.65 3
 
0.1%
440 3
 
0.1%
35.4 3
 
0.1%
59.5 2
 
< 0.1%
174.37 2
 
< 0.1%
Other values (4263) 4321
99.2%
ValueCountFrequency (%)
-1192.2 1
< 0.1%
-811.86 1
< 0.1%
-464.9 1
< 0.1%
-295.09 1
< 0.1%
-227.44 1
< 0.1%
-152.64 1
< 0.1%
-141.48 1
< 0.1%
-134.8 1
< 0.1%
-102.45 1
< 0.1%
-102 1
< 0.1%
ValueCountFrequency (%)
278778.02 1
< 0.1%
259657.3 1
< 0.1%
189735.53 1
< 0.1%
128882.13 1
< 0.1%
123638.18 1
< 0.1%
113855.32 1
< 0.1%
88138.2 1
< 0.1%
65920.12 1
< 0.1%
62924.1 1
< 0.1%
59419.34 1
< 0.1%

Interactions

2023-05-18T18:50:01.887655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:04.237545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:08.724925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:12.615252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:16.726863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:20.556132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:25.305487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:28.974587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:32.872549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:37.533208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:42.727416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:46.832017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:50.689483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:54.100546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:58.260153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:50:02.127844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:04.610416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:09.006353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:12.847909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:16.971948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:20.793490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:25.511011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:29.306373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:33.224082image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:37.913580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:43.098852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:47.063834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:50.891154image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:54.424972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:58.491445image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:50:02.365529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:04.805761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:09.247139image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:13.088504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:17.208183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:21.016939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:25.737801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:29.539795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:33.453279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:38.326831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:43.327283image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:47.281464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:51.132472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:54.754443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:58.713475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:50:02.599297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:05.103375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:09.481485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:13.334048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:17.459997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:21.268067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:26.002003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:29.872015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:33.712305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:38.807628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:43.548299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:47.655877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:51.364907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:55.126965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:58.957661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:50:02.844479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:05.338686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:09.724736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:13.652468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:17.693261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:21.516942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:26.250762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:30.152374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:34.486280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:39.247839image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:43.891586image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:47.922676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:51.607758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:55.431653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:59.208243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:50:03.083886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:05.578273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:09.960282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:13.925661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:17.935241image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:21.752805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:26.486509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:30.403268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:34.855533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:39.680992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:44.127629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:48.267827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:51.840808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:55.783332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:59.460440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:50:03.288882image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:05.780785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:10.177639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:14.303434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:18.162006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:22.035796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:26.689648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:30.620982image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:35.058644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:40.183309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:44.383817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:48.481531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:52.058467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:56.075820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:59.670250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:50:03.529923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:06.169550image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:10.516888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:14.551681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:18.406066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:22.391794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:27.071956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:30.948957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:35.308626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:40.554270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:44.679061image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:48.816063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:52.285853image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:56.328296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:59.944695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:50:03.734750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:06.507950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:10.747175image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:14.964219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:18.642400image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:22.739570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:27.286580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:31.167263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:35.536751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:40.774698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:45.040286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:49.045176image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:52.499216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:56.560359image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:50:00.183847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:50:04.010339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:06.926299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:11.135690image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:15.232025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:18.892383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:23.117729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:27.515758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:31.419791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:35.788925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:41.072635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:45.358915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:49.277625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:52.726651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:56.812521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:50:00.491365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:50:04.228494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:07.228287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:11.358093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:15.556388image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:19.127141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:23.526352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:27.796100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:31.638525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:36.023022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:41.439268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:45.602900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:49.492316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:52.957958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:57.061066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:50:00.721030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:50:04.443892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:07.499339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:11.719742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:15.777621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:19.347361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:24.152206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:28.050128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:31.857260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:36.434764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:41.656987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:45.910122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:49.805198image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:53.161187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:57.280489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:50:00.959479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:50:04.662606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:07.914797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:11.919771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:15.989319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:19.560426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:24.428018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:28.272418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:32.075993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:36.807504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:42.008779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:46.123893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:50.015739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:53.357610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:57.509278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:50:01.177459image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:50:04.908563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:08.195029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:12.189440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:16.247335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:19.912966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:24.837044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:28.536563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:32.325975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:37.092878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:42.256891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:46.378474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:50.251827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:53.588507image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:57.757235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:50:01.429209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:50:05.150360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:08.421723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:12.409883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:16.499285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:20.184023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:25.071401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:28.755316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:32.575956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:37.305639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:42.500221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:46.614855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:50.477034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:53.819930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:49:58.017501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-18T18:50:01.667630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-18T18:50:15.644674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
customer_idrevenue_purcrecency_daysvolume_products_purcassort_products_purcpurchasesavg_period_purcfrequency_purcvolume_basket_size_purcassort_basket_size_purcavg_ticket_purcreturnsvolume_products_retrevenue_retrevenue_real
customer_id1.000-0.0810.008-0.072-0.0090.0000.0050.012-0.107-0.009-0.128-0.045-0.052-0.048-0.076
revenue_purc-0.0811.000-0.4940.9310.7340.811-0.6760.4600.5720.1510.6820.4820.4690.4690.992
recency_days0.008-0.4941.000-0.497-0.467-0.5720.477-0.790-0.178-0.032-0.145-0.253-0.237-0.236-0.496
volume_products_purc-0.0720.931-0.4971.0000.7320.764-0.6360.4690.7230.1910.6270.4440.4370.4280.926
assort_products_purc-0.0090.734-0.4670.7321.0000.662-0.5360.4180.4380.6120.4290.3520.3310.3300.740
purchases0.0000.811-0.5720.7640.6621.000-0.8510.5280.154-0.1210.1720.4780.4540.4540.810
avg_period_purc0.005-0.6760.477-0.636-0.536-0.8511.000-0.570-0.1200.130-0.132-0.387-0.369-0.369-0.675
frequency_purc0.0120.460-0.7900.4690.4180.528-0.5701.0000.175-0.0050.1390.2530.2360.2350.463
volume_basket_size_purc-0.1070.572-0.1780.7230.4380.154-0.1200.1751.0000.4490.8160.1670.1820.1660.569
assort_basket_size_purc-0.0090.151-0.0320.1910.612-0.1210.130-0.0050.4491.0000.418-0.075-0.077-0.0790.160
avg_ticket_purc-0.1280.682-0.1450.6270.4290.172-0.1320.1390.8160.4181.0000.2230.2280.2270.675
returns-0.0450.482-0.2530.4440.3520.478-0.3870.2530.167-0.0750.2231.0000.9730.9760.455
volume_products_ret-0.0520.469-0.2370.4370.3310.454-0.3690.2360.182-0.0770.2280.9731.0000.9890.434
revenue_ret-0.0480.469-0.2360.4280.3300.454-0.3690.2350.166-0.0790.2270.9760.9891.0000.433
revenue_real-0.0760.992-0.4960.9260.7400.810-0.6750.4630.5690.1600.6750.4550.4340.4331.000

Missing values

2023-05-18T18:50:05.486166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-18T18:50:06.036831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idrevenue_purcrecency_daysvolume_products_purcassort_products_purcpurchasesavg_period_purcfrequency_purcvolume_basket_size_purcassort_basket_size_purcavg_ticket_purcreturnsvolume_products_retrevenue_retrevenue_real
0178505391.210372.0001733.00021.00034.0001.0000.09150.9710.618158.5651.00040.000102.5805288.630
1130473232.59056.0001390.000105.0009.00052.8330.024154.44411.667359.1777.00035.000143.4903089.100
2125836705.3802.0005028.000114.00015.00026.5000.040335.2007.600447.0252.00050.00076.0406629.340
313748948.25095.000439.00024.0005.00092.6670.01387.8004.800189.6500.0000.0000.000948.250
415100876.000333.00080.0001.0003.00020.0000.00826.6670.333292.0003.00022.000240.900635.100
5152914623.30025.0002102.00061.00014.00026.7690.037150.1434.357330.2365.00029.00071.7904551.510
6146885630.8707.0003621.000148.00021.00019.2630.056172.4297.048268.1376.000399.000523.4905107.380
7178095411.91016.0002057.00046.00012.00039.6670.032171.4173.833450.9932.00041.00067.0605344.850
81531160767.9000.00038194.000567.00091.0004.1910.243419.7146.231667.77927.000474.0001348.56059419.340
9160982005.63087.000613.00034.0007.00047.6670.01987.5714.857286.5190.0000.0000.0002005.630
customer_idrevenue_purcrecency_daysvolume_products_purcassort_products_purcpurchasesavg_period_purcfrequency_purcvolume_basket_size_purcassort_basket_size_purcavg_ticket_purcreturnsvolume_products_retrevenue_retrevenue_real
43471600012393.7002.0005110.0009.0003.000400.0001.0001703.3333.0004131.2330.0000.0000.00012393.700
4348151953861.0002.0001404.0001.0001.000400.0000.3331404.0001.0003861.0000.0000.0000.0003861.000
434914087194.4202.000251.00061.0001.000400.0000.333251.00061.000194.4201.0001.00012.750181.670
435014204161.0302.00082.00036.0001.000400.0000.33382.00036.000161.0300.0000.0000.000161.030
435115471469.4802.000266.00067.0001.000400.0000.333266.00067.000469.4800.0000.0000.000469.480
435213436196.8901.00076.00012.0001.000400.0000.50076.00012.000196.8900.0000.0000.000196.890
435315520343.5001.000314.00018.0001.000400.0000.500314.00018.000343.5000.0000.0000.000343.500
435413298360.0001.00096.0002.0001.000400.0000.50096.0002.000360.0000.0000.0000.000360.000
435514569227.3901.00079.00010.0001.000400.0000.50079.00010.000227.3900.0000.0000.000227.390
435612713794.5500.000505.00037.0001.000400.0001.000505.00037.000794.5500.0000.0000.000794.550